


import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


__UpperCamelCase     		=							logging.getLogger(__name__)







@dataclass
class 							lowerCamelCase__						:






			"""simple docstring"""






			_UpperCamelCase		:       str
			_UpperCamelCase		:       List[str]
			_UpperCamelCase		:       Optional[List[str]]







@dataclass
class 							lowerCamelCase__						:






			"""simple docstring"""






			_UpperCamelCase		:       List[int]
			_UpperCamelCase		:       List[int]
			_UpperCamelCase		:       Optional[List[int]] 			=		None
			_UpperCamelCase		:       Optional[List[int]] 			=		None







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Dict 			=		'train'
			_UpperCamelCase		:       Any 			=		'dev'
			_UpperCamelCase		:       Any 			=		'test'







class 							lowerCamelCase__						:






			"""simple docstring"""






			@staticmethod
			def 					snake_case__					(				snake_case   ,    snake_case  ):

						'''simple docstring'''


						raise NotImplementedError




			@staticmethod
			def 					snake_case__					(				snake_case  ):

						'''simple docstring'''


						raise NotImplementedError







			@staticmethod
			def 					snake_case__					(				snake_case   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case=False   ,    snake_case="[CLS]"   ,    snake_case=1   ,    snake_case="[SEP]"   ,    snake_case=False   ,    snake_case=False   ,    snake_case=0   ,    snake_case=0   ,    snake_case=-100   ,    snake_case=0   ,    snake_case=True   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							{label: i for i, label in enumerate(snake_case  )}

						UpperCamelCase__       =							[]
						for ex_index, example in enumerate(snake_case  ):
									if ex_index % 10000 == 0:
												logger.info("Writing example %d of %d"   ,    snake_case   ,    len(snake_case  )  )

									UpperCamelCase__       =							[]
									UpperCamelCase__       =							[]
									for word, label in zip(example.words   ,    example.labels  ):
												UpperCamelCase__       =							tokenizer.tokenize(snake_case  )

												# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
												if len(snake_case  ) > 0:
															tokens.extend(snake_case  )
															# Use the real label id for the first token of the word, and padding ids for the remaining tokens
															label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(snake_case  ) - 1)  )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
									UpperCamelCase__       =							tokenizer.num_special_tokens_to_add()
									if len(snake_case  ) > max_seq_length - special_tokens_count:
												UpperCamelCase__       =							tokens[: (max_seq_length - special_tokens_count)]
												UpperCamelCase__       =							label_ids[: (max_seq_length - special_tokens_count)]

									# The convention in BERT is:
									# (a) For sequence pairs:
									#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
									#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
									# (b) For single sequences:
									#  tokens:   [CLS] the dog is hairy . [SEP]
									#  type_ids:   0   0   0   0  0     0   0
									#
									# Where "type_ids" are used to indicate whether this is the first
									# sequence or the second sequence. The embedding vectors for `type=0` and
									# `type=1` were learned during pre-training and are added to the wordpiece
									# embedding vector (and position vector). This is not *strictly* necessary
									# since the [SEP] token unambiguously separates the sequences, but it makes
									# it easier for the model to learn the concept of sequences.
									#
									# For classification tasks, the first vector (corresponding to [CLS]) is
									# used as the "sentence vector". Note that this only makes sense because
									# the entire model is fine-tuned.
									tokens += [sep_token]
									label_ids += [pad_token_label_id]
									if sep_token_extra:
												# roberta uses an extra separator b/w pairs of sentences
												tokens += [sep_token]
												label_ids += [pad_token_label_id]
									UpperCamelCase__       =							[sequence_a_segment_id] * len(snake_case  )

									if cls_token_at_end:
												tokens += [cls_token]
												label_ids += [pad_token_label_id]
												segment_ids += [cls_token_segment_id]
									else:
												UpperCamelCase__       =							[cls_token] + tokens
												UpperCamelCase__       =							[pad_token_label_id] + label_ids
												UpperCamelCase__       =							[cls_token_segment_id] + segment_ids

									UpperCamelCase__       =							tokenizer.convert_tokens_to_ids(snake_case  )

									# The mask has 1 for real tokens and 0 for padding tokens. Only real
									# tokens are attended to.
									UpperCamelCase__       =							[1 if mask_padding_with_zero else 0] * len(snake_case  )

									# Zero-pad up to the sequence length.
									UpperCamelCase__       =							max_seq_length - len(snake_case  )
									if pad_on_left:
												UpperCamelCase__       =							([pad_token] * padding_length) + input_ids
												UpperCamelCase__       =							([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
												UpperCamelCase__       =							([pad_token_segment_id] * padding_length) + segment_ids
												UpperCamelCase__       =							([pad_token_label_id] * padding_length) + label_ids
									else:
												input_ids += [pad_token] * padding_length
												input_mask += [0 if mask_padding_with_zero else 1] * padding_length
												segment_ids += [pad_token_segment_id] * padding_length
												label_ids += [pad_token_label_id] * padding_length

									assert len(snake_case  ) == max_seq_length
									assert len(snake_case  ) == max_seq_length
									assert len(snake_case  ) == max_seq_length
									assert len(snake_case  ) == max_seq_length

									if ex_index < 5:
												logger.info("*** Example ***"  )
												logger.info("guid: %s"   ,    example.guid  )
												logger.info("tokens: %s"   ,    " ".join([str(snake_case  ) for x in tokens]  )  )
												logger.info("input_ids: %s"   ,    " ".join([str(snake_case  ) for x in input_ids]  )  )
												logger.info("input_mask: %s"   ,    " ".join([str(snake_case  ) for x in input_mask]  )  )
												logger.info("segment_ids: %s"   ,    " ".join([str(snake_case  ) for x in segment_ids]  )  )
												logger.info("label_ids: %s"   ,    " ".join([str(snake_case  ) for x in label_ids]  )  )

									if "token_type_ids" not in tokenizer.model_input_names:
												UpperCamelCase__       =							None

									features.append(
									    InputFeatures(
									        input_ids=snake_case   ,    attention_mask=snake_case   ,    token_type_ids=snake_case   ,    label_ids=snake_case  )  )
						return features


if is_torch_available():
						import torch
						from torch import nn
						from torch.utils.data import Dataset







						class 							lowerCamelCase__						(				UpperCAmelCase   ):






									"""simple docstring"""






									_UpperCamelCase		:       List[InputFeatures]
									_UpperCamelCase		:       int 			=		nn.CrossEntropyLoss().ignore_index




									def __init__(				self   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case = None   ,    snake_case=False   ,    snake_case = Split.train   ,    ):

												'''simple docstring'''


												UpperCamelCase__       =							os.path.join(
												    snake_case   ,    "cached_{}_{}_{}".format(mode.value   ,    tokenizer.__class__.__name__   ,    str(snake_case  )  )   ,    )

												# Make sure only the first process in distributed training processes the dataset,
												# and the others will use the cache.
												UpperCamelCase__       =							cached_features_file + ".lock"
												with FileLock(snake_case  ):
															if os.path.exists(snake_case  ) and not overwrite_cache:
																		logger.info(F'''Loading features from cached file {cached_features_file}'''  )
																		UpperCamelCase__       =							torch.load(snake_case  )
															else:
																		logger.info(F'''Creating features from dataset file at {data_dir}'''  )
																		UpperCamelCase__       =							token_classification_task.read_examples_from_file(snake_case   ,    snake_case  )
																		# TODO clean up all this to leverage built-in features of tokenizers
																		UpperCamelCase__       =							token_classification_task.convert_examples_to_features(
																		    snake_case   ,    snake_case   ,    snake_case   ,    snake_case   ,    cls_token_at_end=bool(model_type in ["xlnet"]  )   ,    cls_token=tokenizer.cls_token   ,    cls_token_segment_id=2 if model_type in ["xlnet"] else 0   ,    sep_token=tokenizer.sep_token   ,    sep_token_extra=snake_case   ,    pad_on_left=bool(tokenizer.padding_side == "left"  )   ,    pad_token=tokenizer.pad_token_id   ,    pad_token_segment_id=tokenizer.pad_token_type_id   ,    pad_token_label_id=self.pad_token_label_id   ,    )
																		logger.info(F'''Saving features into cached file {cached_features_file}'''  )
																		torch.save(self.features   ,    snake_case  )




									def __len__(				self  ):

												'''simple docstring'''


												return len(self.features  )







									def __getitem__(				self   ,    snake_case  ):

												'''simple docstring'''


												return self.features[i]


if is_tf_available():
						import tensorflow as tf







						class 							lowerCamelCase__						:






									"""simple docstring"""






									_UpperCamelCase		:       List[InputFeatures]
									_UpperCamelCase		:       int 			=		-100




									def __init__(				self   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case = None   ,    snake_case=False   ,    snake_case = Split.train   ,    ):

												'''simple docstring'''


												UpperCamelCase__       =							token_classification_task.read_examples_from_file(snake_case   ,    snake_case  )
												# TODO clean up all this to leverage built-in features of tokenizers
												UpperCamelCase__       =							token_classification_task.convert_examples_to_features(
												    snake_case   ,    snake_case   ,    snake_case   ,    snake_case   ,    cls_token_at_end=bool(model_type in ["xlnet"]  )   ,    cls_token=tokenizer.cls_token   ,    cls_token_segment_id=2 if model_type in ["xlnet"] else 0   ,    sep_token=tokenizer.sep_token   ,    sep_token_extra=snake_case   ,    pad_on_left=bool(tokenizer.padding_side == "left"  )   ,    pad_token=tokenizer.pad_token_id   ,    pad_token_segment_id=tokenizer.pad_token_type_id   ,    pad_token_label_id=self.pad_token_label_id   ,    )

												def gen():
															for ex in self.features:
																		if ex.token_type_ids is None:
																					yield (
																					    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																					    ex.label_ids,
																					)
																		else:
																					yield (
																					    {
																					        "input_ids": ex.input_ids,
																					        "attention_mask": ex.attention_mask,
																					        "token_type_ids": ex.token_type_ids,
																					    },
																					    ex.label_ids,
																					)

												if "token_type_ids" not in tokenizer.model_input_names:
															UpperCamelCase__       =							tf.data.Dataset.from_generator(
															    snake_case   ,    ({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)   ,    (
															        {"input_ids": tf.TensorShape([None]  ), "attention_mask": tf.TensorShape([None]  )},
															        tf.TensorShape([None]  ),
															    )   ,    )
												else:
															UpperCamelCase__       =							tf.data.Dataset.from_generator(
															    snake_case   ,    ({"input_ids": tf.intaa, "attention_mask": tf.intaa, "token_type_ids": tf.intaa}, tf.intaa)   ,    (
															        {
															            "input_ids": tf.TensorShape([None]  ),
															            "attention_mask": tf.TensorShape([None]  ),
															            "token_type_ids": tf.TensorShape([None]  ),
															        },
															        tf.TensorShape([None]  ),
															    )   ,    )




									def 					snake_case__					(				self  ):

												'''simple docstring'''


												UpperCamelCase__       =							self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features  )  )  )

												return self.dataset




									def __len__(				self  ):

												'''simple docstring'''


												return len(self.features  )







									def __getitem__(				self   ,    snake_case  ):

												'''simple docstring'''


												return self.features[i]






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						super().__init__()
						self.register_modules(unet=snake_case   ,    scheduler=snake_case  )







			@torch.no_grad()
			def __call__(				self   ,    snake_case = 1   ,    snake_case = None   ,    snake_case = 50   ,    snake_case = "pil"   ,    snake_case = True   ,    **snake_case   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							torch.randn(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,    generator=snake_case   ,    )
						UpperCamelCase__       =							image.to(self.device  )

						# set step values
						self.scheduler.set_timesteps(snake_case  )

						for t in self.progress_bar(self.scheduler.timesteps  ):
									# 1. predict noise model_output
									UpperCamelCase__       =							self.unet(snake_case   ,    snake_case  ).sample

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									UpperCamelCase__       =							self.scheduler.step(snake_case   ,    snake_case   ,    snake_case  ).prev_sample

						UpperCamelCase__       =							(image / 2 + 0.5).clamp(0   ,    1  )
						UpperCamelCase__       =							image.cpu().permute(0   ,    2   ,    3   ,    1  ).numpy()
						if output_type == "pil":
									UpperCamelCase__       =							self.numpy_to_pil(snake_case  )

						if not return_dict:
									return (image,), "This is a local test"

						return ImagePipelineOutput(images=snake_case  ), "This is a local test"


